Classifiers are used in image processing to classify a given pixel or region within a set of image data into one of a limited number of predefined categories. Classifiers have been successfully employed in the field of medical image processing, specifically in the effort to classify different categories of tissue in medical images. For instance, in intravascular ultrasound (IVUS) imaging, classifiers are applied to blood vessel images to distinguish between various tissue types such as vulnerable plaque, blood and calcified tissue. The process of classifying image regions into the appropriate categories is referred to as image segmentation.
Typically, classifiers are generic computational procedures that are customizable for a given classification problem. Examples of classifier methodologies include, but are not limited to, Bayesian classifiers, k-nearest neighbor classifiers and neural network classifiers. Examples of previous classification techniques are set forth in the following patents, each of which are incorporated herein by reference: U.S. Pat. No. 6,757,412 issued to Parsons et al., which describes a tissue classification technique based on thermal imaging; U.S. Pat. Nos. 6,266,435, 6,477,262 and 6,574,357 issued to Wang, which describe tissue diagnosis and classification based on radiological imaging and U.S. Pat. No. 5,260,871 issued to Goldberg, which describes neural network tissue classification based on ultrasound imaging,
Classifiers can be customized to identify the presence of a particular distinctive region. The customization process is referred to as training and is accomplished by providing a large number of exemplary images of the distinctive region to the generic classifier. The classifier extracts features associated with each image and learns the association between the feature and the known distinctive region. Once the training phase is complete, the classifier can be used to classify regions within new images by extending the previously learned association.
In most practical applications, the classifier output is at best correct only in a statistical sense. Given the very large number of potential image patterns that can be encountered, the design of an accurate classifier, i.e., a classifier that is capable of properly identifying the distinctive region when present while at the same time properly distinguishing the distinctive region from other regions having a similar appearance, can be very difficult. Furthermore, these complex classifiers can consume significant processing resources in their implementation, which can hinder data processing times and real-time imaging procedures.
Accordingly, there is a need for reduced complexity classifiers capable of achieving a high accuracy rate.